meta-analysis

Meta-Analysis Skill

Meta-Analysis Types

Type	RoB Tool	Statistical Model	Reporting Guideline
DTA (diagnostic test accuracy)	QUADAS-3 (QUADAS-2 for legacy reviews)	Bivariate / HSROC	PRISMA-DTA
Intervention (treatment effect)	RoB 2 (RCT) / ROBINS-I (NRSI)	Random-effects (REML, Hartung-Knapp CI)	PRISMA 2020
Prognostic factor (association of one factor with outcome)	QUIPS	Random-effects	PRISMA 2020
Prediction model (model performance)	PROBAST	Random-effects	PRISMA 2020
Observational (prevalence/association)	NOS / JBI	Random-effects	MOOSE

If the type is ambiguous (DTA vs intervention), ask the user to clarify before proceeding.

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Workflow Phases

Phase 1: Protocol Development

Goal: Produce a PROSPERO-ready protocol document. Write the protocol, extraction forms and manuscript text in English whatever language the user writes in — PROSPERO records and the target journals are English-language.

1. Research question: PIRD (Population, Index test, Reference standard, Diagnosis) for DTA; PICO (Population, Intervention, Comparator, Outcome) for intervention.

2. DTA only — do QUADAS-3 phases 1 and 2 now, not at risk-of-bias time. They are review-level and belong in the protocol: phase 1 states the synthesis question(s) (population, index test(s), target condition — a review may have more than one); phase 2 defines the ideal test accuracy trial for each (objective, participants, index test(s), definition of the target condition, analysis). Every later risk-of-bias and applicability judgement is made against that trial. Write the review-specific guidance for answering each signalling question here too, with clinical and methodological input, and publish it as a web appendix. Defining the ideal trial after seeing the studies is a judgement fitted to the results, not an assessment. See references/checklists/QUADAS3.md.

3. Eligibility criteria: study design, population, index test / intervention, comparator / reference standard, outcomes (Se/Sp for DTA; effect size for intervention), and exclusion criteria with justification.

4. Search plan: at least 3 databases — PubMed, Embase, and Cochrane CENTRAL (add Scopus / Web of Science as needed); a Boolean strategy from the PIRD/PICO components; a grey-literature plan (conference abstracts, trial registries); language restrictions and date range stated explicitly, with justification.

5. RoB plan: tool by type (table above), at least 2 independent assessors, and the disagreement-resolution method (consensus, third reviewer).

6. Synthesis plan: bivariate random-effects (Reitsma) or HSROC (Rutter & Gatsonis) for DTA; random-effects for intervention (Phase 6); heterogeneity, subgroup / sensitivity, and publication-bias plans.

7. PROSPERO registration document: read ${CLAUDE_SKILL_DIR}/references/PROSPERO_template.md and follow its field guide, word limits, output format (Markdown + DOCX via pandoc), and Common Pitfalls Checklist. Save to the project's 7_Submission/ or equivalent directory.

   • Registration-ID format gate. A PROSPERO ID is CRD42 + 9 digits (14 characters total), e.g. CRD42024500001. Validate any ID that appears in the manuscript or registration doc with grep -oE 'CRD42[0-9]+' and assert a 14-character length / ^CRD42\d{9}$ — a 15-character ID (a stray digit) is a transcription error a reviewer will check against the live record.
   • Review-type selection. Pick the least-wrong portal review type for the actual design and state any portal constraint in the protocol. A descriptive single-arm proportion synthesis is not an "Intervention review"; choosing that type only to satisfy a portal field contradicts a later GRADE / effect-certainty statement. Whatever certainty language the protocol commits to (GRADE vs "evidence statements only") must match the manuscript verbatim — a guideline-style "we recommend" is not licensed by a descriptive review type.

Phase 2: Search Strategy

Goal: Develop and validate reproducible search strategies.

1. Build search blocks from the PIRD/PICO components and execute them per database with /search-lit (PubMed: MeSH + free text; Embase: Emtree + free text; further databases as the protocol specifies).
2. Report per PRISMA-S (Rethlefsen et al. 2021, PMID:33499930): one section per database with date of search, number of results, and any limits applied.
3. Merge and deduplicate into a single spreadsheet: by DOI first, then PMID. Save raw counts for the PRISMA flow.

Phase 3: Screening & Selection

Goal: Systematic title/abstract and full-text screening with two independent reviewers. Read references/phase3_screening_detail.md when executing a round (exclusion-code sets, AI pre-screening template and Methods boilerplate) or when a 3f/3f.5 gate fires (set algebra, reconciliation table).

3a. Round 1 — title/abstract (single reviewer). Define the exclusion codes from the protocol. Mark every record INCLUDE / EXCLUDE / MAYBE with a reason code → round1_{date}.tsv.

3b. Round 2 — dual independent title/abstract. A second independent reviewer (or AI as a documented second-pass tool with human verification) re-screens all R1 records. Report Cohen's κ in Methods. round2_tag = INCLUDE / EXCLUDE / MAYBE (MAYBE = disagreement or either reviewer flagged uncertainty), plus round2_reason.

3c. Round 3 — adjudication (first reviewer). MAYBE records first, then INCLUDE records for a brief confirmation pass → round3_decision (plus round3_reason only when overturning R2). Optional AI pre-screening may compress the effort, but AI suggestions are not decisions: the reviewer independently confirms or overturns every one.

3d. Round 4 — full text (/fulltext-retrieval) for round3_decision = INCLUDE: full-text exclusion codes, two independent reviewers, Cohen's κ, consensus or a third reviewer. Flag comparative studies for priority extraction.

3e. PRISMA flow. Track counts at every stage (R1 → R2 → R3 → R4 → final included); draw it with /make-figures once final.

3f. Post-consensus count reconciliation gate (MANDATORY before Phase 5 write-up). Reconcile counts from the raw ID sets, never from prose summaries, into one source-of-truth file:

python "${CLAUDE_SKILL_DIR}/scripts/screening_reconcile.py" \
  --screening 2_Screening/fulltext_screening.tsv \
  --consensus 2_Screening/consensus_decisions.tsv \
  --table1 6_Tables/table1_studies.csv \
  --output 2_Screening/screening_consensus.json

Downstream stages consume screening_consensus.json for counts and ID sets; the Markdown consensus document remains the human explanation. Three hard rules:

1. List the narrative-only IDs explicitly. The highest-yield red flag is a numeric claim ("10 narrative-only studies") that does not match the enumerable set (A ∪ C) \ B \ T.
2. No "N → M" transition without ID receipts. "k rose from 30 to 32 after FLAG consensus" must cite the added/removed IDs. A transition claim with no enumerable ID set is a P0 and blocks the Phase 5 hand-off.
3. STAGE_TRANSFER_LOSS is a P0. Exit 1 when a record is included at screening but absent from the consensus artifact altogether — no adjudication was ever recorded. An exclusion is a decision; silence is a gap. Never let it settle into narrative-only.

Two more gates at 3f; a non-zero exit from either blocks the Phase 5 write-up:

# every applied exclusion code vs the *registered* eligibility criteria
python3 ${CLAUDE_SKILL_DIR}/scripts/check_exclusion_code_validity.py --protocol 0_Protocol/protocol.md --screening 2_Screening/*.tsv --strict
# DI-6: PRISMA numbers on 5 surfaces vs YAML SSOT; re-run on every revision touching PRISMA numbers
python3 ${CLAUDE_SKILL_DIR}/scripts/prisma_5way_consistency.py --ssot prisma.yaml

Exclusion-code verdicts: CODE_CONTRADICTS_ELIGIBILITY (a code excludes a design the protocol includes — bulk study loss no other gate can see), CODE_NOT_REGISTERED, CODE_RENUMBERED. Verdicts, NOT_ASSESSED cases and known limits of all 3f gates: references/phase3_screening_detail.md §3f.

• Known limit: PRISMA 5-way still skips after_dedup → full_text_assessed; optional prisma2020: counts add report-level identities (§3f).

3f.5 Pool composition lock (MANDATORY at adjudication freeze). Once 3f passes, freeze the pool into a single source-of-truth YAML that every downstream artifact can be checked against:

cp "${CLAUDE_SKILL_DIR}/templates/FINAL_POOL_LOCK.yaml.template" 2_Data/FINAL_POOL_LOCK.yaml
# fill counts + UID lists from 3f, compute the SHA-256 over the sorted UID list,
# and COMMIT THE LOCK before any Phase 4 extraction

• Never re-derive k included from the extraction TSV at manuscript build time — always reference final_pool_n from the lock.
• Aggregate patient/lesion totals are locked too, not just study counts. Distinguish arm-separable from both-arm rows: a study contributing one arm must not have its full-cohort count folded into a pooled total. A hand-carried headline total that does not re-derive from the locked per-study values is a P0.
• A late post-freeze change to the pool is a formal PROSPERO amendment: file it, re-freeze as FINAL_POOL_LOCK_v2.yaml, and propagate to every artifact.

Phase 4: Data Extraction

Goal: Create standardized extraction forms and extract 2x2 or effect-size data. Read references/phase4_extraction_detail.md when building the form (DTA / intervention field lists), when an AI draft was shared, for the optional extract_assist.py suggestions (AI_SUGGESTED, a human confirms each before dta_extraction_qc.py), or when a QC flag fires.

4.0 Entry gate (MANDATORY) — pool composition lock ↔ adjudication TSV. Before any extraction work begins, confirm the round-3 adjudication TSV and FINAL_POOL_LOCK.yaml (Phase 3f.5) agree on which UIDs are included:

python "${CLAUDE_SKILL_DIR}/scripts/check_pool_consistency.py" \
    --lock 2_Data/FINAL_POOL_LOCK.yaml \
    --adjudication-tsv 2_Screening/round3_adjudication.tsv \
    --decision-col round3_decision --uid-col uid \
    --include-labels "INCLUDE,INCLUDE_MIXED" \
    --out qc/pool_consistency.json

The gate fails closed: any UID disagreement blocks extraction. Resolve by re-freezing the lock with the corrected UID set (and propagating downstream) or by correcting a mis-labelled TSV row. Do NOT proceed with a mismatch — the extraction matrix will not align with the locked pool, and the drift surfaces as a fabrication-grade red flag at peer review.

# before the first extraction row (DI-1): comparative arm rows never live in R-script comments
python3 ${CLAUDE_SKILL_DIR}/scripts/extraction_consensus_log_init.py --output 2_Data/extraction_consensus_log.md

Failure-mode cross-ref → references/data_integrity_checklist.md DI-1~DI-5 are mandatory during extraction (2x2 arm-swap, KM audit trail, methodology mismatch, PRISMA 5-way drift, single-source k).

Extraction form. Read ${CLAUDE_SKILL_DIR}/references/empirical_lessons.md before designing it. For high-impact radiology / medical AI targets use ${CLAUDE_SKILL_DIR}/templates/extraction_form_v2.md: its dual-extractor, source-page-reference, and verbatim-quote columns close the 2x2 cell-swap and cohort-overlap blind spots.

AI-drafted starting document — treat as hallucination-suspect. If a mentor or collaborator shared an AI-drafted study list, 2x2 set, or effect estimates (even flagged "for reference only"): save it with a _DO_NOT_USE_VERBATIM suffix and re-verify every N, denominator, event count, OR/CI, and author/year against the source PDF. Trust hierarchy: source PDF + own analysis stdout > the mentor's direct text > the attached AI draft — never promote a draft up that ladder.

4b. Special cases (KM reconstruction, composite exposure). When studies report outcomes only as Kaplan-Meier curves, or the intervention is a composite of techniques, load ${CLAUDE_SKILL_DIR}/references/phase4_km_composite.md for the WebPlotDigitizer → IPDfromKM procedure (cite Guyot et al. 2012, doi:10.1186/1471-2288-12-9) and the 4-path composite-exposure decision tree. Pre-specify a sensitivity analysis excluding composite-exposure studies.

Cross-verification (≥2 independent reviewers). Report inter-reviewer agreement (% or Cohen's κ) at title/abstract and full-text stages. Verify denominator consistency — the denominator may differ across outcomes within one study, so for each outcome back-calculate event ÷ denominator and confirm it reproduces the paper's reported percentage. Distinguish KM-curve estimates from raw event counts and record the data source (Table / KM / text). Log every consensus decision in 2_Data/extraction_consensus_log.md, then lock the dataset; later changes need a dated justification. If 2x2 cells are missing, suggest contacting the authors or a sensitivity analysis with imputed values.

4c. Extraction QC & cohort overlap. After dual-extractor consensus, run both before locking:

# 2x2 cell integrity: validates TP/FN/TN/FP against source-reported sens/spec (catches arm-swap)
python3 "${CLAUDE_SKILL_DIR}/scripts/dta_extraction_qc.py" \
  --input 2_Extraction/extraction.csv --tolerance 0.02 \
  --out 2_Extraction/qc/dta_extraction_qc.tsv

# cohort overlap: shared public DB / same institution+period / same first author ±2y
python3 "${CLAUDE_SKILL_DIR}/scripts/cohort_overlap_check.py" \
  --input 2_Extraction/studies.csv --enrich \
  --out 2_Extraction/qc/cohort_overlap.md

Any FLAG_SWAP / FLAG_MISMATCH requires third-reviewer adjudication before Phase 6. A confirmed flag is not resolved until the extraction form itself is edited — a flag corrected only in a review note silently re-enters synthesis, so re-run the QC and confirm zero open flags before locking. HIGH-confidence overlap pairs require a Limitations acknowledgment plus a sensitivity analysis excluding one of the pair.

Phase 5: Risk of Bias Assessment

Goal: Guide structured RoB assessment with the appropriate tool.

DTA: this phase runs QUADAS-3 phases 3–6 (flow diagram, identify the estimates to assess, assess, overall judgement). Phases 1–2 — the synthesis question and the ideal test accuracy trial — were written in Phase 1 above. If they were not, stop and write them before judging anything; they are the comparator every judgement is made against.

Select the tool by meta-analysis type (see table above), then read its checklist:

Tool	Checklist File
QUADAS-3 (DTA, current)	${CLAUDE_SKILL_DIR}/references/checklists/QUADAS3.md
QUADAS-2 (DTA, legacy)	${CLAUDE_SKILL_DIR}/references/checklists/QUADAS2.md
RoB 2 (RCT)	${CLAUDE_SKILL_DIR}/references/checklists/RoB2.md
ROBINS-I (NRSI)	${CLAUDE_SKILL_DIR}/references/checklists/ROBINS_I.md
PROBAST (Prediction)	${CLAUDE_SKILL_DIR}/references/checklists/PROBAST.md
NOS (Observational)	${CLAUDE_SKILL_DIR}/references/checklists/NOS.md
JBI (Case Series)	${CLAUDE_SKILL_DIR}/references/checklists/JBI_Case_Series.md

For AI/ML prediction models, also apply PROBAST+AI extensions.

Output: Summary table + traffic light plot (use /make-figures).

Phase 6: Statistical Synthesis

Goal: Execute meta-analysis and generate publication-ready outputs.

Failure-mode cross-ref → references/data_integrity_checklist.md DI-6/DI-7/DI-9 are the consistency gate (CSV ↔ script ↔ prose; single-source k; 3-way numeric reconciliation before Stage 4).

Always use R (packages: meta, metafor, mada); every reported estimate, CI, p-value, and sample size comes from executed code output (Phase 6b audits this). Never guess dataset column names or codings — if a mapping is uncertain, output [VERIFY: variable_name] and ask the user to confirm against the data dictionary.

Analysis family	Primary tool	Key output
DTA	mada::reitsma() (bivariate)	Pooled Se/Sp + SROC with confidence/prediction regions
Intervention	meta::metagen() / meta::metabin()	Pooled OR/RR, τ² + I² + prediction interval, measure-matched funnel test (k ≥ 10), leave-one-out
Dual (comparative + single-arm)	metabin + metaprop	PRIMARY vs SECONDARY per pre-specified protocol

Read ${CLAUDE_SKILL_DIR}/references/phase6_statistical_synthesis.md before running the pooled analysis — full R code templates (companion: ${CLAUDE_SKILL_DIR}/references/r_templates.md), the dual-approach decision table (comparative vs single-arm), practical cautions (method.tau, HK CI, zero-cell correction), publication-bias test power, the sensitivity-analysis menu, and error-handling rules. Write the pooled estimates, heterogeneity statistics, and k for each analysis, taken from the executed R output, to analysis/meta_analysis_outputs.json.

Three checks before the pool is written up — each is a Methods sentence, not only a setting. R and detail in the same reference:

1. Is the event rare? A pooled event rate < 1%, or any zero-event arm, moves the analysis off the inverse-variance default onto Peto / Mantel-Haenszel without a zero-cell correction / GLMM. Inverse-variance methods including DerSimonian-Laird are to be avoided for rare events, and so are 0.5 continuity corrections with them.
2. Why this model? Fixed vs random is a judgment about whether one common true effect exists — never derived from Cochran's Q or I². "A random-effects model was used because I² was 65%" is a reviewer catch, not a rationale.
3. Does one study contribute several correlated effect sizes? Multiple outcomes, readers, thresholds, or time points from the same participants need one pre-specified estimate per study, a multivariate model, or robust variance estimation — not independent pooling.

Before pooling transcribed ratio CIs (and back-deriving SEs), check them (columns study, measure, estimate, lower, upper, optional ci_level in percent and ci_method); RATIO_CI_IMPOSSIBLE is Major, RATIO_CI_ASYMMETRIC (Minor) means verify the transcription or declare ci_method:

python3 ${CLAUDE_SKILL_DIR}/scripts/check_ratio_ci_symmetry.py --extraction 2_Extraction/extraction.csv --out 2_Extraction/qc/ratio_ci.json --strict

Phase 6b: Post-Analysis Source Fidelity Audit (MANDATORY)

Goal: Catch numerical hallucinations that survived the forward pipeline (CSV → .R → manuscript).

When it runs: every time Phase 6 outputs change (first draft, revision, reviewer-requested re-analysis) — including "minor" re-runs. The precedent: in a minor revision-era re-analysis, a safety outcome's arm-level events (and so its p-value) were reported direction-reversed because a Fisher matrix() was hand-typed from a misread source Table while the extraction CSV was correct. Every downstream artifact echoed the wrong number, so internal consistency checks passed; only a random back-check against the primary paper caught it.

Non-negotiable rules:

1. No hand-typed numerical matrices when a CSV exists. Use read.csv(...) + subset / filter; never copy a 2x2 table from a paper into matrix(c(...), ...) by eye. If hand entry is truly unavoidable (e.g., text-only extraction), the matrix, c(), or data.frame line MUST carry a comment citing the exact CSV row + column OR the exact primary-source Table/Page coordinate:

   # source: data_extraction_final.csv row <N> (<first-author> <year>), cols <event_arm1>=0, <event_arm2>=1
   # verified against primary source Table <X>, page <P>
   fisher.test(matrix(c(0, 45, 1, 55), nrow = 2, byrow = FALSE))
   

2. Comparative-arm subsets are a separate consensus-log row. When one study's arm-specific values are used in a comparative analysis while its full cohort appears elsewhere, extraction_consensus_log.md must carry an explicit row for the arm-specific values. Pooled totals and arm-specific values MUST NOT share a row.

3. Random 3-claim back-check before closing Phase 6. After the forest/funnel/subgroup outputs stabilize, randomly sample 3 numerical claims from the draft Results and trace each back to (a) the R output log and (b) the original paper's Table/Figure. Record it in peer_review_<vN>_internal.md:

   Claim (manuscript line)	R output file:line	Primary source (paper, Table/Fig, page)	Match?

   A single mismatch is a P0 blocker — do not advance to Phase 7 until resolved.

4. Revision-introduced numbers must be tagged. Any new number added after v1 — including numbers from a new comparative / subgroup / sensitivity script — MUST be wrapped inline as [VERIFY-CSV] in the manuscript until the Phase 2.5a audit in /self-review clears it.

5. Sensitivity analyses must be recomputed on the modified data, not copied. Every reported effect size in a sensitivity / leave-one-out / erosion / alternative-model analysis (Cohen's dz/f, AUC, OR, HR, β, sens/spec, ICC) MUST be re-derived from the modified dataset. If a sensitivity-table effect size is identical to the primary analysis to two decimals across ≥4 values while the underlying means/SDs/counts differ, the recomputation may not have run (small leave-one-out shifts can round to the same value, so confirm from the script output rather than assume) and the primary values may have been transcribed — re-run the script on the modified data.

6. A "fixed" / "resolved" audit note requires re-run evidence, not a claim. A number recorded as fixed, resolved, or corrected counts only with a timestamp and the stdout / output-file line showing the corrected value, or the commit that changed it. A bare "fixed in v10" does NOT clear the finding — re-run the script and attach the output. The outcome-denominator cross-check (/self-review Phase 2.5b, the cohort-arithmetic / pool-lock assertions) must pass against the current outputs before any "fixed" status is accepted.

Phase 7: GRADE / Certainty of Evidence

Goal: Assess certainty of the body of evidence.

DTA: GRADE-DTA — risk of bias (from QUADAS-3, or QUADAS-2 for a legacy review), indirectness (applicability concerns), inconsistency (heterogeneity), imprecision (wide CIs, small sample), publication bias. Intervention: standard GRADE.

Certainty is assessed per outcome, not once for the review. The domains resolve differently for each outcome — one pooled from 12 studies with narrow CIs and one pooled from 3 with a wide CI do not share a rating, and a single review-level "moderate certainty" sentence tells a reader nothing about the outcome they came for. Rate every outcome carried into the Summary of Findings table, and state the reason for each downgrade (which domain, why), not only the resulting label.

Output: Summary of Findings table — one row per outcome, carrying the pooled estimate with its precision alongside the certainty rating (high / moderate / low / very low).

Phase 8: Reporting & Manuscript

Goal: Generate PRISMA-compliant manuscript sections.

Failure-mode cross-ref → references/submission_package_drift.md — apply the _build.sh pattern + DO_NOT_EDIT_HERE gate when staging multi-journal submission folders.

Re-read references/empirical_lessons.md before submission.

1. Check reporting compliance: /check-reporting with PRISMA-DTA (bundled copy: references/checklists/PRISMA_DTA.md) or PRISMA 2020, then a second, separate run over the abstract with PRISMA 2020 for Abstracts (PRISMA_2020_Abstracts.md, 12 items, its own denominator). Report that score separately: item 2 of the main checklist only defers to it, so a manuscript can satisfy all 42 main-text items and still fail most of the twelve, and folding them into one total is how they stay invisible.

2. Write the manuscript: /write-paper with the meta-analysis type → manuscript/manuscript.md. Never generate references from memory; use /search-lit for all citations.

3. Figures (/make-figures): PRISMA flow diagram, forest plots (paired for DTA), SROC curve (DTA), funnel plot (Deeks' for DTA — see DTA pitfalls), RoB summary (traffic light plot).

4. Tables: characteristics of included studies; 2x2 data per study (DTA); RoB assessment results; Summary of findings / GRADE table (one row per outcome — Phase 7).

5. The items published radiology SR/MAs most often drop — check these by hand before the compliance run. Park 2022 (Korean J Radiol; PMID:35213097) scored 24 SR/MAs (18 with meta-analysis) against PRISMA 2020, with each item's percentage on its own denominator (MA-only items out of 18), and found 24 of 42 items reported by fewer than 80%:

   PRISMA item	What is missing	Observed
   20a	For each synthesis, a brief summary of the contributing studies' characteristics and risk of bias — not one global paragraph covering all pools	0/24
   27	Data availability: which of the extraction forms, extracted data, analysis dataset, and analytic code are public, and where	0/24
   24a–c	Registration number, where the protocol can be read, and any amendment — an explicit "not registered" satisfies 24a	0/24
   22 / 15	Certainty of evidence per outcome, and the method used to assess it	9%
   13f / 20d	Sensitivity analysis: method and result	28% (5/18)
   18	Risk of bias per study, shown study-by-study rather than as a pooled proportion	32%
   13d	Rationale for the synthesis model (see Phase 6 check 2)	28% (5/18)
   16b	Studies that look eligible but were excluded, cited individually with the reason	25%
   Abstract #3, #12	Eligibility criteria and registration inside the structured abstract	0/24 each

   If the PROSPERO ID is missing, flag it as a limitation but continue.

6. Data availability statement: name what is being shared (extraction template, locked dataset, analysis code, RoB judgments) and where — repository, DOI, or supplementary file. "Available from the corresponding author on reasonable request" satisfies few journals now and no longer satisfies item 27. If a Zenodo DOI is minted post-acceptance, references/post_submission_release_ops.md covers propagating it back into this statement.

7. Supplementary & analysis-code pre-submission gate (before Phase 9 circulation and before portal upload). Presence of the 8-file package (Empirical Lesson 5) is necessary but not sufficient — each item must also be reviewer-ready:

   • De-scaffold: strip internal-QC / tool artifacts — raw /check-reporting output ("Assessed by: <tool>", JSON blocks, "READY FOR SUBMISSION" verdicts, action-item lists), search-development planning docs (decision logs, expected-yield estimates, [Check on execution] placeholders, version-history dev notes), and stale version stamps. Ship a clean PRISMA 2020 checklist (27-item / 42-subitem table only) and an executed-method search-strategy doc, not the working drafts.
   • Blind: remove author names/initials and sibling-project cross-references ("Designed by: <name>", "identical to a sibling review") — same standard as the blinded manuscript.
   • Cross-consistency: every supplementary number matches the main text — PRISMA counts, pool k/N, the Cochrane/CENTRAL search description, RoB counts.
   • Reproducible, self-contained analysis code: run it from a clean copy of the bundle. It must read the bundled locked dataset (not an out-of-bundle path), write to the working directory, and regenerate every pool in the results table. A hard-coded study-id subset that drifts from the manuscript (a pool over k=7 while the manuscript reports k=9) is a P0 — fix and re-run; never ship stale code or figures derived from it.
   • Supplementary-only review pass: the manuscript self-review does not see the supplement; mirror /self-review Phase 2.5c–2.5d (reference + cross-reference QC) over the supplementary files.

8. Submission gates (on Phase 8 pre-submission and every journal retarget; a non-zero exit blocks submission):

   • /sync-submission SR-MA gate: the supplementary package matches all 8 files in templates/supplementary_8file_checklist.md (PRISMA, PROSPERO, search strategy, exclusion list, extraction table, per-study x per-domain RoB, subgroup forests, sensitivity / publication bias); AI Disclosure is present (cross-link /peer-review Phase 2A P8); no duplicate PMID/DOI in the cite list (/verify-refs Gate 5).
   • DI-8 tag gate — fails if VERIFY-CSV/TODO/FIXME/XXX survive in 7_Manuscript, supplement, SUBMISSION, etc.: bash ${CLAUDE_SKILL_DIR}/scripts/tag_cleanup_gate.sh
   • SPD package integrity — checksum-based drift detection between the master manuscript and the built SUBMISSION/{journal}/ folder (journal-editable files — cover letter, response, MANIFEST, DO_NOT_EDIT_HERE.md — are auto-excluded). On the first build per journal run python3 ${CLAUDE_SKILL_DIR}/../sync-submission/scripts/verify_package_integrity.py --record --journal <name>, then --verify --journal <name> before every re-submission.
   • ICMJE COI forms for every author: ${CLAUDE_SKILL_DIR}/references/icmje_coi_guide.md.

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Phase 9: Co-author Circulation

Goal: Pre-submission circulation to co-authors and a senior methodologist / reviewer, with a bounded review window and a controlled attachment scope.

Trigger: Phase 8 is complete, and the draft has cleared the Phase 6b source-fidelity audit.

Summary: Reply to the prior-version email thread to preserve In-Reply-To continuity (v1 → v2 → v3 tracked in one place). Attach the manuscript body with figures inline and, for v≥2, a change summary — exclude graphical abstract, cover letter, COI forms, and supplementary until the target journal is confirmed. TO = corresponding author + one senior methodologist; CC = remaining co-authors. Set a 7-day deadline (5 business days + weekend). Ask the corresponding author for target-journal preference, reviewer candidates, and cover-letter framing.

Load-on-demand procedural detail (thread continuity, attachment scope rationale, size-to-method table, journal-undetermined framing, response-tracking log): ${CLAUDE_SKILL_DIR}/references/phase9_circulation.md.

Failure-mode cross-ref → references/review_orchestration.md RO-1~RO-5 (dual-rating completeness, defensive-tone bias audit, response-matrix numeric tracking, 2nd-reviewer availability blocking).

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Phase 10: Self-Audit Recovery (v{N} → v{N+1} sprint)

Goal: When an audit uncovers a structural data or protocol-application error, withdraw the current version, rebuild, and re-circulate with a transparent audit trail.

Trigger conditions (any one):

#	Trigger	Source
T1	Extraction CSV ↔ primary source disagreement for a cell feeding a pooled/subgroup estimate or reported proportion	Phase 6b audit
T2	Included/excluded study violates the pre-specified criteria on re-read	Protocol review
T3	Hand-typed numerical literal in the analysis script traces to a wrong value	Phase 6b audit
T4	PROSPERO protocol ↔ delivered analysis disagreement on outcome, subgroup, or eligibility	Protocol ↔ analysis diff
T5	Dual-reviewer consensus record ↔ locked dataset disagreement on inclusion	Consensus log diff

Non-negotiable rule: if the trigger fires after Phase 9 circulation but before journal submission, withdraw the current version within 24 hours. Reviewer discovery is a strictly worse failure mode than self-withdrawal.

Sprint: read ${CLAUDE_SKILL_DIR}/references/phase10_recovery.md and run its 12 steps, from 10.1 (audit log at qc/audit_vN_to_vNplus1.md) through the PROSPERO amendment (application correction, not criteria change) and re-circulation in the Phase 9 thread to 10.12 (post-recovery loop).

Failure-mode cross-ref → references/post_submission_release_ops.md Gate 4 covers reject/revise Zenodo versioning, tag-cleanup gate, and re-target workflow (avoid "new version" misuse on re-target).

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DTA-Specific Pitfalls (Always Check)

Pitfall	Problem	Solution
Separate pooling of Se/Sp	Ignores correlation	Use bivariate/HSROC model
Ignoring threshold effect	False heterogeneity	Judge from the SROC plot and the bivariate Se–FPR correlation (Spearman is descriptive only)
Standard funnel plot for DTA	Inappropriate	Use Deeks' funnel plot
I-squared only for heterogeneity	Doesn't capture threshold effect	Use prediction region on SROC
Missing GRADE	Common omission in DTA MA	Apply GRADE-DTA. If <4 studies, assess each domain narratively and state the limitation explicitly
Partial verification bias	Inflates sensitivity	QUADAS-3 3.2 (target condition assessed in all participants). QUADAS-3 has no Flow & Timing domain — that was QUADAS-2
Differential verification bias	Distorts both Se and Sp	QUADAS-3 3.3 (target condition assessed the same way in all participants)
Unevaluable results excluded	Biases accuracy estimates	Report intent-to-diagnose analysis

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Small Study Considerations

When the number of included studies is small (< 10):

• Bivariate/HSROC model may not converge (warn the user when a DTA review has fewer than 4 studies) — consider univariate random-effects as fallback
• Publication bias tests are underpowered — state this limitation
• Subgroup/meta-regression analysis not recommended
• Wide prediction regions expected — emphasize uncertainty in conclusions
• Consider narrative synthesis as alternative/complement